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Binary and multi-class motor imagery using Renyi entropy for feature extraction

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Abstract

Entropy, the complexity measures for time series, has found numerous successful applications in brain signal analysis such as detection of epileptic seizure and monitoring the depth of anesthesia. Renyi entropy generalizes the well-known Shannon entropy, and hence providing better flexibility in application to real data. The objective of this paper is to evaluate the effectiveness of Renyi entropy as feature extraction method for motor imagery (MI)-based brain–computer interface (BCI). In this paper, Renyi entropy has been implemented in MI systems of various settings using BCI competition data sets. The classification accuracy of Renyi entropy in all data sets is benchmarked against common spatial pattern (CSP), the state-of-the-art feature extraction method. For common binary class data sets, Renyi entropy achieves an accuracy of approximately 3.4 % higher for BCI Competition II Data Set III and 0.87 % lower for BCI Competition III Data Set I, and there is no difference in accuracy for BCI Competition III Data Set IVc when compared against conventional CSP. In small sample setting, the average classification accuracy using Renyi entropy is approximately 3.4 and 0.3 % higher as compared to conventional CSP and best performing variant of regularized CSP, respectively. In addition, Renyi entropy is also compared to other chaos-inspired feature extraction methods, namely Katz and Higuchi which are implemented for MI systems by earlier researchers. The effect of implementing Renyi entropy on multiple narrower frequency sub-bands is also investigated in this study. In multi-class setting, Renyi entropy shows no statistical difference (p = 0.6022, paired t test) in accuracy when compared to the algorithm of the BCI competition winner. However, Renyi entropy has an advantage of requiring only one single application of feature extraction regardless of the number of classes, while multiple implementation of CSP is required as CSP is originally designed for binary class problem. The successful application of Renyi entropy in all the aforementioned settings indicates that Renyi entropy is a viable feature extraction alternative for MI-based BCI systems.

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References

  1. Birbaumer N (2006) Breaking the silence: brain–computer interfaces (BCI) for communication and motor control. Psychophysiology 43(6):517–532

    Article  Google Scholar 

  2. Mak JN, Wolpaw JR (2009) Clinical applications of brain–computer interfaces: current state and future prospects. IEEE Rev Biomed Eng 2:187–199

    Article  Google Scholar 

  3. Long J, Li Y, Wang H, Yu T, Pan J, Li F (2012) A hybrid brain computer interface to control the direction and speed of a simulated or real wheelchair. IEEE Trans Neural Syst Rehabil Eng 20(5):720–729

    Article  Google Scholar 

  4. Mcmullen DP, Member S, Hotson G, Katyal KD, Wester BA, Fifer MS, Mcgee TG, Harris A, Johannes MS, Vogelstein RJ, Ravitz AD, Anderson WS, Thakor NV, Crone NE, Member S (2014) Demonstration of a semi-autonomous hybrid brain–machine interface using human intracranial EEG, eye tracking, and computer vision to control a robotic upper limb prosthetic. IEEE Trans Neural Syst Rehabil Eng 22(4):784–796

    Article  Google Scholar 

  5. Serby H, Yom-Tov E, Inbar GF (2005) An improved P300-based brain–computer interface. IEEE Trans Neural Syst Rehabil Eng 13(1):89–98

    Article  Google Scholar 

  6. Nicolas-Alonso LF, Gomez-Gil J (2012) Brain computer interfaces, a review. Sensors 12(2):1211–1279

    Article  Google Scholar 

  7. McFarland DJ, Miner LA, Vaughan TM, Wolpaw JR (2000) Mu and beta rhythm topographies during motor imagery and actual movements. Brain Topogr 12(3):177–186

    Article  Google Scholar 

  8. Blankertz B, Dornhege G, Krauledat M, Müller K-R, Curio G (2007) The non-invasive berlin brain–computer interface: fast acquisition of effective performance in untrained subjects. Neuroimage 37(2):539–550

    Article  Google Scholar 

  9. Bradberry TJ, Gentili RJ, Contreras-Vidal JL (2011) Fast attainment of computer cursor control with noninvasively acquired brain signals. J Neural Eng 8(3):036010

    Article  Google Scholar 

  10. McFarland DJ, Sarnacki WA, Wolpaw JR (2010) Electroencephalographic (EEG) control of three-dimensional movement. J Neural Eng 7(3):036007

    Article  Google Scholar 

  11. Doud AJ, Lucas JP, Pisansky MT, He B (2011) Continuous three-dimensional control of a virtual helicopter using a motor imagery based brain–computer interface. PLoS One 6(10):e26322

    Article  Google Scholar 

  12. Zhang Z, Jung T-P, Makeig S, Rao BD (2013) Compressed sensing of EEG for wireless telemonitoring with low energy consumption and inexpensive hardware. IEEE Trans Biomed Eng 60(1):221–224

    Article  Google Scholar 

  13. Sawan M, Salam MT, Le Lan J, Kassab A, Gelinas S, Vannasing P, Lesage F, Lassonde M, Nguyen DK (2013) Wireless recording systems: from noninvasive EEG-NIRS to invasive EEG devices. IEEE Trans Biomed Circuits Syst 7(2):186–195

    Article  Google Scholar 

  14. Jurcak V, Tsuzuki D, Dan I (2007) 10/20, 10/10, and 10/5 systems revisited: their validity as relative head-surface-based positioning systems. Neuroimage 34(4):1600–1611

    Article  Google Scholar 

  15. Boostani R, Moradi MH (2004) A new approach in the BCI research based on fractal dimension as feature and Adaboost as classifier. J Neural Eng 1(4):212–217

    Article  Google Scholar 

  16. Brunner C, Billinger M, Vidaurre C, Neuper C (2011) A comparison of univariate, vector, bilinear autoregressive, and band power features for brain–computer interfaces. Med. Biol. Eng. Comput. 49(11):1337–1346

    Article  Google Scholar 

  17. Ang KK, Chin ZY, Wang C, Guan C, Zhang H (2012) Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Front Neurosci 6:39

    Article  Google Scholar 

  18. Li Y, Koike Y (2010) A real-time BCI with a small number of channels based on CSP. Neural Comput Appl 20(8):1187–1192

    Article  Google Scholar 

  19. Samek W, Vidaurre C, Müller K-R, Kawanabe M (2012) Stationary common spatial patterns for brain–computer interfacing. J Neural Eng 9(2):026013

    Article  Google Scholar 

  20. Blankertz B, Müller K-R, Krusienski DJ, Schalk G, Wolpaw JR, Schlögl A, Pfurtscheller G, Millán JDR, Schröder M, Birbaumer N (2006) The BCI competition. III: validating alternative approaches to actual BCI problems. IEEE Trans Neural Syst Rehabil Eng 14(2):153–159

    Article  Google Scholar 

  21. Tangermann M, Müller K-R, Aertsen A, Birbaumer N, Braun C, Brunner C, Leeb R, Mehring C, Miller KJ, Müller-Putz GR, Nolte G, Pfurtscheller G, Preissl H, Schalk G, Schlögl A, Vidaurre C, Waldert S, Blankertz B (2012) Review of the BCI competition IV. Front Neurosci 6:55

    Article  Google Scholar 

  22. Zmeskal O, Dzik P, Vesely M (2013) Entropy of fractal systems. Comput Math Appl 66(2):135–146

    Article  MathSciNet  MATH  Google Scholar 

  23. Andino SLG, Menendez RGDP, Thut G, Spinelli L, Blanke O, Michel CM, Seeck M, Landis T (2000) Measuring the complexity of time series: an application to neurophysiological signals. Hum Brain Mapp 11(1):46–57

    Article  Google Scholar 

  24. Faust O, Bairy MG (2012) Nonlinear analysis of physiological signals: a review. J Mech Med Biol 12(04):1240015

    Article  Google Scholar 

  25. Gao J, Hu J (2013) Fast monitoring of epileptic seizures using recurrence time statistics of electroencephalography. Front Comput Neurosci 7:122

    Article  Google Scholar 

  26. Mammone N, Inuso G, La Foresta F, Versaci M, Morabito FC (2011) Clustering of entropy topography in epileptic electroencephalography. Neural Comput Appl 20(6):825–833

    Article  Google Scholar 

  27. Fraiwan L, Lweesy K, Khasawneh N, Wenz H, Dickhaus H (2012) Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier. Comput Methods Programs Biomed 108(1):10–19

    Article  Google Scholar 

  28. Faust O, Ang PCA, Puthankattil SD, Joseph PK (2014) Depression diagnosis support system based on EEG signal entropies. J Mech Med Biol 14(03):1450035

    Article  Google Scholar 

  29. Brockmeier AJ, Santanna E, Sanchez-Giraldo LG, Principe JC (2014) Projentropy: using entropy to optimize spatial projections. In: IEEE International conference on acoustics, speech and signal processing, pp 4538–4542

  30. Loo CK, Samraj A, Lee GC (2011) Evaluation of methods for estimating fractal dimension in motor imagery-based brain computer interface. Discret Dyn Nat Soc 2011:1–9

    Article  MathSciNet  Google Scholar 

  31. Obermaier B, Neuper C (2001) Information transfer rate in a five-classes brain–computer interface. IEEE Trans Neural Syst Rehabil Eng 9(3):283–288

    Article  Google Scholar 

  32. Dornhege G, Blankertz B (2004) Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms. IEEE Trans Biomed Eng 51(6):993–1002

    Article  Google Scholar 

  33. Ang KK, Chin ZY, Zhang H, Guan C (2008) Filter bank common spatial pattern (FBCSP) in brain–computer interface. In: International Joint Conference on Neural Networks (IJCNN), pp. 2390–2397

  34. Blankertz B, Muller K, Curcio G, Vaughan TM, Schalk G, Wolpaw JR, Schlögl A, Neuper C, Pfurtscheller G, Hinterberger T, Schröder M, Birbaumer N (2004) The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials. IEEE Trans Biomed Eng 51(6):1044–1051

    Article  Google Scholar 

  35. Shenoy P, Krauledat M, Blankertz B, Rao RPN, Müller K-R (2006) Towards adaptive classification for BCI. J Neural Eng 3(1):R13–R23

    Article  Google Scholar 

  36. Hsu W-Y (2011) Continuous EEG signal analysis for asynchronous BCI application. Int J Neural Syst 21(4):335–350

    Article  Google Scholar 

  37. McFarland DJ, McCane LM, David SV, Wolpaw JR (1997) Spatial filter selection for EEG-based communication. Electroencephalogr Clin Neurophysiol 103(3):386–394

    Article  Google Scholar 

  38. Paramanathan P, Uthayakumar R (2008) Application of fractal theory in analysis of human electroencephalographic signals. Comput Biol Med 38(3):372–378

    Article  MATH  Google Scholar 

  39. Raghavendra BS, Dutt DN (2009) A note on fractal dimensions of biomedical waveforms. Comput Biol Med 39(11):1006–1012

    Article  Google Scholar 

  40. Wang Q, Sourina O, Nguyen MK (2011) Fractal dimension based neurofeedback in serious games. Vis Comput 27(4):299–309

    Article  Google Scholar 

  41. Eguiraun H, Lopez-de-ipina K, Martinez I (2014) Application of entropy and fractal dimension analyses to the pattern recognition of contaminated fish responses in aquaculture. Entropy 16:6133–6151

    Article  Google Scholar 

  42. Esteller R, Vachtsevanos G, Echauz J, Litt B (2001) A comparison of waveform fractal dimension algorithms. IEEE Trans Circuits Syst Fundam Theory Appl 48(2):177–183

    Article  Google Scholar 

  43. Ahmadlou M, Adeli H, Adeli A (2011) Fractality and a wavelet-chaos-methodology for EEG-based diagnosis of Alzheimer disease. Alzheimer Dis Assoc Disord 25(1):85–92

    Article  Google Scholar 

  44. Blankertz B, Tomioka R, Lemm S, Kawanabe M, Muller K (2008) Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process Mag 25(1):41–56

    Article  Google Scholar 

  45. Ormos M, Zibriczky D (2014) Entropy-based financial asset pricing. PLoS One 9(12):e115742

    Article  Google Scholar 

  46. Katz MJ (1988) Fractals and the analysis of waveforms. Comput Biol Med 18(3):145–156

    Article  Google Scholar 

  47. Hoffmann U, Vesin J-M, Ebrahimi T, Diserens K (2008) An efficient P300-based brain–computer interface for disabled subjects. J Neurosci Methods 167(1):115–125

    Article  Google Scholar 

  48. Lotte F, Congedo M, Lécuyer A, Lamarche F, Arnaldi B (2007) A review of classification algorithms for EEG-based brain–computer interfaces. J Neural Eng 4(2):R1–R13

    Article  Google Scholar 

  49. Lei X, Yang P, Yao D (2009) An empirical Bayesian framework for brain–computer interfaces. IEEE Trans Neural Syst Rehabil Eng 17(6):521–529

    Article  Google Scholar 

  50. Xu P, Yang P, Lei X, Yao D (2011) An enhanced probabilistic LDA for multi-class brain computer interface. PLoS One 6(1):e14634

    Article  Google Scholar 

  51. Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1):37–46

    Article  Google Scholar 

  52. Schlogl A, Brunner C (2008) BioSig: a free and open source software library for BCI research. Computer 41(10):44–50

    Article  Google Scholar 

  53. Ramoser H, Müller-gerking J, Pfurtscheller G (2000) Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng 8(4):441–446

    Article  Google Scholar 

  54. Raghavendra BS, Dutt DN (2010) Computing fractal dimension of signals using multiresolution box-counting method. World Acad Sci Eng Technol 6(1):50–65

    Google Scholar 

  55. Castiglioni P (2010) What is wrong in Katz’s method? Comments on: ‘a note on fractal dimensions of biomedical waveforms’. Comput Biol Med 40(11–12):950–952

    Article  Google Scholar 

  56. Lu H, Eng H-L, Guan C, Plataniotis KN, Venetsanopoulos AN (2010) Regularized common spatial pattern with aggregation for EEG classification in small-sample setting. IEEE Trans Biomed Eng 57(12):2936–2946

    Article  Google Scholar 

  57. Gouy-Pailler C, Congedo M (2010) Nonstationary brain source separation for multiclass motor imagery. IEEE Trans Biomed Eng 57(2):469–478

    Article  Google Scholar 

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Authors and Affiliations

  1. Advanced Engineering Platform and School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia

    Chea-Yau Kee & S. G. Ponnambalam

  2. Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Chu-Kiong Loo

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  1. Chea-Yau Kee
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  2. S. G. Ponnambalam
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  3. Chu-Kiong Loo
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Correspondence to S. G. Ponnambalam.

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Kee, CY., Ponnambalam, S.G. & Loo, CK. Binary and multi-class motor imagery using Renyi entropy for feature extraction. Neural Comput & Applic 28, 2051–2062 (2017). https://doi.org/10.1007/s00521-016-2178-y

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